feat: add Phase 3 hybrid search with Synesis reranking

Implement hybrid search pipeline combining vector, fulltext, and graph
search across Neo4j, with cross-attention reranking via Synesis
(Qwen3-VL-Reranker-2B) `/v1/rerank` endpoint.

- Add SearchService with vector, fulltext, and graph search strategies
- Add SynesisRerankerClient for multimodal reranking via HTTP API
- Add search API endpoint (POST /search/) with filtering by library,
  collection, and library_type
- Add SearchRequest/Response serializers and image search results
- Add "nonfiction" to library_type choices
- Consolidate reranker stack from two models to single Synesis service
- Handle image analysis_status as "skipped" when analysis is unavailable
- Add comprehensive tests for search pipeline and reranker client

README.md

@@ -25,8 +25,7 @@ This **content-type awareness** flows through every layer: chunking strategy, em
 |-----------|-----------|---------|
 | **Knowledge Graph** | Neo4j 5.x | Relationships + vector storage (no dimension limits) |
 | **Multimodal Embeddings** | Qwen3-VL-Embedding-8B | Text + image + video in unified vector space (4096d) |
-| **Multimodal Re-ranking** | Qwen3-VL-Reranker-8B | Cross-attention precision scoring |
-| **Text Fallback** | Qwen3-Reranker (llama.cpp) | Text-only re-ranking via GGUF |
+| **Multimodal Re-ranking** | Synesis (Qwen3-VL-Reranker-2B) | Cross-attention precision scoring via `/v1/rerank` |
 | **Web Framework** | Django 5.x + DRF | Auth, admin, API, content management |
 | **Object Storage** | S3/MinIO | Original content + chunk text storage |
 | **Async Processing** | Celery + RabbitMQ | Document embedding, graph construction |